Optimized Deployment Method of Edge Computing Network Service Function Chain Delay Combined with Deep Reinforcement Learning
A delay-optimized Service Function Chain (SFC) deployment approach is proposed by combining deep reinforcement learning with the delay-based Dijkstra pathfinding algorithm for the problem of resource-constrained edge networks and low end-to-end delay tolerance for service flows. Firstly, an attention mechanism-based Sequence to Sequence (Seq2Seq) agent network and a delay-based Dijkstra pathfinding algorithm are designed for generating Virtual Network Function(VNF) deployments and link mapping for SFC, while the constraint problem of the delay optimization model is considered and incorporated into the reinforcement learning objective function using Lagrangian relaxation techniques; Secondly, to assist the network agent in converging quickly, a baseline evaluator network is used to assess the expected reward value of the deployment strategy; Finally, in the testing phase, the deployment strategy of the agent is improved by reducing the probability of convergence of the network to a local optimum through greedy search and sampling techniques. Comparison experiments show that the method reduces the latency by about 10％ and 86.3％ than the First-Fit algorithm and TabuSearch algorithm, respectively, and is about 74.2％ and 84.4％ more stable than these two algorithms in the case of limited network resources. This method provides a more stable end-to-end service with lower latency, enabling a better experience for latency-sensitive services.
Service Function Chain(SFC) deploymentDeep reinforcement learningEdge networksEnd-to-end latency
万方数据
维普
